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DimQ1/nemotron-3.5-asr-streaming-0.6b-onnx-int8-cpu
nemotron-3.5-asr-streaming-0.6b-onnx-int8-cpu is a automatic speech recognition model from DimQ1. Use it when you need speech turned into text. The card lists the license as cc-by-nc-4.0.
INT8 k-quant quantized ONNX model converted from NVIDIA Nemotron 3.5 ASR Streaming 0.6B using Microsoft Olive.
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Updated Jun 30, 2026
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From the Hugging Face model README
INT8 k-quant quantized ONNX model converted from NVIDIA Nemotron 3.5 ASR Streaming 0.6B using Microsoft Olive.
Optimized for fast CPU inference with ONNX Runtime GenAI. Uses NVIDIA's recommended 0.56s chunk size for best latency/accuracy trade-off.
| Property | Value |
|---|---|
| Base model | NVIDIA Nemotron 3.5 ASR Streaming 0.6B |
| Encoder layers | 24 |
| Hidden size | 1024 |
| Quantization | INT8 k-quant (block_size=32, accuracy_level=4) |
| Chunk size | 0.56s (8,960 samples @ 16kHz) |
| Sample rate | 16,000 Hz |
| Vocab size | 13,088 |
| Languages | 80+ (multilingual) |
| VAD | Silero VAD included |
| Total size | ~1,021 MB |
using NemotronSpeech;
using var session = new ModelSession(
modelPath: "DimQ1/nemotron-3.5-asr-streaming-0.6b-onnx-int8-cpu",
executionProvider: "cpu",
langId: "11", // Russian
useVad: true
);
import onnxruntime_genai as og
import numpy as np
model = og.Model("DimQ1/nemotron-3.5-asr-streaming-0.6b-onnx-int8-cpu")
processor = og.StreamingProcessor(model)
tokenizer = og.Tokenizer(model)
params = og.GeneratorParams(model)
generator = og.Generator(model, params)
generator.set_runtime_option("lang_id", "11") # Russian
chunk = np.zeros(8960, dtype=np.float32) # 0.56s @ 16kHz
inputs = processor.process(chunk)
if inputs is not None:
generator.set_inputs(inputs)
while not generator.is_done():
generator.generate_next_token()
cd converter
python src/optimize.py \
--model-name nemotron-3.5-asr-streaming-0.6b.nemo \
--encoder-precision int8 \
--execution-provider cpu \
--output-dir build/onnx_models_int8_cpu
See nemotron-speech-csharp for the full C# demo app with VoiceType WPF UI.
| File | Size |
|---|---|
encoder.onnx + .data | 922 MB |
decoder.onnx + .data | 57 MB |
joint.onnx + .data | 36 MB |
silero_vad.onnx | 2.1 MB |
tokenizer.json | 0.7 MB |
| Model | Precision | Encoder | Total | Quality |
|---|---|---|---|---|
| FP32 | FP32 | 2,380 MB | 2,479 MB | ⭐ Best |
| INT8 | INT8 k-quant | 922 MB | 1,021 MB | Good |
| INT4 | INT4 k-quant | 658 MB | 757 MB | ⚡ Fastest |
This model inherits cc-by-nc-4.0 from the base NVIDIA Nemotron model.